87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
# Created by MSXYZ (AI-assisted)
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import torch
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import torch.nn.functional as F
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class VideoAdaptiveAA:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE",),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
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"edge_threshold": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}),
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"blur_radius": ("INT", {"default": 1, "min": 1, "max": 5, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_aa"
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CATEGORY = "CustomPostProcess"
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def apply_aa(self, images, strength, edge_threshold, blur_radius):
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img = images.permute(0, 3, 1, 2)
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has_alpha = img.shape[1] == 4
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if has_alpha:
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alpha = img[:, 3:4, :, :]
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img_rgb = img[:, :3, :, :]
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else:
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img_rgb = img
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img_gray = (
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0.2 * img_rgb[:, 0:1, :, :]
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+ 0.7 * img_rgb[:, 1:2, :, :]
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+ 0.1 * img_rgb[:, 2:3, :, :]
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)
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# Kenarları belirle
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get_sobel_x = torch.tensor(
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[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32
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).view(1, 1, 3, 3).to(img.device)
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get_sobel_y = torch.tensor(
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[[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32
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).view(1, 1, 3, 3).to(img.device)
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edges_x = F.conv2d(img_gray, get_sobel_x, padding=1)
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edges_y = F.conv2d(img_gray, get_sobel_y, padding=1)
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edges = torch.sqrt(edges_x**2 + edges_y**2 + 1e-6)
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mask = (edges > edge_threshold).float()
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mask = torch.clamp(mask * strength, 0.0, 1.0)
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kernel_size = blur_radius * 2 + 1
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img_padded = F.pad(
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img_rgb,
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(blur_radius, blur_radius, blur_radius, blur_radius),
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mode="replicate",
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)
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blurred = F.avg_pool2d(img_padded, kernel_size=kernel_size, stride=1, padding=0)
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result_rgb = img_rgb * (1.0 - mask) + blurred * mask
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result_rgb = torch.clamp(result_rgb, 0.0, 1.0)
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if has_alpha:
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result = torch.cat([result_rgb, alpha], dim=1)
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else:
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result = result_rgb
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result = result.permute(0, 2, 3, 1)
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return (result,)
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NODE_CLASS_MAPPINGS = {
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"VideoAdaptiveAA": VideoAdaptiveAA
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"VideoAdaptiveAA": "✨ Video Adaptive Anti-Aliasing"
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} |